GDL

Journal from gdlhub / 2017-08-16 13:41:29
Oleh : Ines Boujelben, Salma Jamoussi, Abdelmajid Ben Hamadou, King Saud University
Dibuat : 2014-12-16, dengan 1 file

Keyword : Hybrid method Relation extraction Named entity Machine learning Genetic algorithm Rule-based method
Url : http://www.sciencedirect.com/science/article/pii/S1319157814000287
Sumber pengambilan dokumen : web

Relation extraction is a very useful task for several natural language processing applications, such as automatic summarization and question answering. In this paper, we present our hybrid approach to extracting relations between Arabic named entities. Given that Arabic is a rich morphological language, we build a linguistic and learning model to predict the positions of words that express a semantic relation within a clause. The main idea is to employ linguistic modules to ameliorate the results that are obtained from a machine learning-based method.


Our method achieves encouraging performance. The empirical results indicate that the hybrid approach outperformed both the rule-based system (by 12%) and the machine learning-based approaches (by 9%) in terms of the F-score, to achieve 75.2% when applied to the same standard testing dataset, ANERCorp.

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